The diagnostically superior information available from data acquired from current imaging systems enables the detection of potential problems at earlier and more treatable stages. Given the vast quantity of detailed data acquirable from imaging systems, various algorithms must be developed to efficiently and accurately process image data. With the aid of computers, advances in image processing are generally performed on digital or digitized images.
Digital acquisition systems for creating digital images include digital X-ray film radiography, computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”), ultrasound (“US”) and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”). Digital images can also be created from analog images by, for example, scanning analog images, such as typical x-rays, into a digitized form. However, the large amount of data in digital images is generally difficult and tedious for a human, such as a physician, to interpret without additional aid. Computer-aided diagnosis (“CAD”) systems play a critical role in aiding the human, especially in the visualization, segmentation, detection, registration, and reporting of medical pathologies.
Digital images are created from an array of numerical values representing a property (such as a grey scale value or magnetic field strength) associable with an anatomical location points referenced by a particular array location. The set of anatomical location points comprises the domain of the image. In 2-D digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art. The 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images. The pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels.
Once anatomical regions and structures are constructed and evaluated by analyzing pixels and/or voxels, subsequent processing and analysis exploiting regional characteristics and features can be applied to relevant areas, thus improving both accuracy and efficiency of the imaging system.
One of the more critical CAD tasks includes the screening and early detection of various types of cancer from a volume data (e.g., a CT volume data). For instance, lung cancer is the leading cause of deaths among all cancers in the United States and around the world. A patient diagnosed with lung cancer has an average five-year survival rate of only 14%. On the other hand, if lung cancer is diagnosed in stage I, the patient's expected five-year survival rate dramatically increases to between 60 and 70 percent. Other cancers, such as colon cancer, have also shown a decrease in mortality rates resulting from the early detection and removal of cancerous tumors. Pathologies are typically spherical or hemispherical in geometric shape. In many cases, these sphere-like pathologies are attached to linear or piece-wise linear surfaces. Unfortunately, existing methods generally do not detect characteristic symptoms of various cancers until the advanced stages of the disease. Therefore, a primary goal in advancing preventive cancer screening is to provide for earlier detection of the characteristic symptoms.